Abstract
Traditional DNS tunnel detection methods based on load analysis and traffic monitoring have a high false positive rate and cannot effectively respond to new DNS tunnel attacks. To this end, a log-based statistical method is proposed to detect DNS tunnel attacks. Compare and analyse the differences between DNS tunnel attack behaviours and normal DNS parsing behaviour from the perspective of DNS sessions, extract the multi-dimensional features dominated by cache hit ratios, compose DNS session evaluation vectors, and use random forest classification algorithms to construct DNS session evaluation vector detection classifiers A DNS tunnel attack detection model based on characteristic statistical behaviours is established. The actual test results show that this method has a small false positive rate and low false negative rate, and it also has a high detection ability for unknown DNS tunnel attacks.
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